A reverse data-driven energy estimation approach for evaluating renewable energy systems implementation in New York City

Abstract

Urban areas are responsible for a significant part of greenhouse gas emissions. Megacities, especially, have cumulative impacts due to different anthropogenic heat sources and urban heat island phenomena. It is therefore vital to replace fossil fuel burning heating sources with renewable sources in the current building stock. Renewable energy sources can be applied through building energy retrofits, which need to be technically and economically feasible. It is, however, always a complex matter to have a fast and reasonably accurate energy use prediction for the retrofit feasibility study. New York City was chosen for this research due to the wide variety of publicly accessible data, including high-quality metered energy and high-resolution geomatics data. This study proposes a reverse method to predict hourly building energy performance according to the total metered energy use. Buildings were divided into different categories based on type and vintage. A robust relation between building categories and hourly energy use was found. Hourly energy trend was defined to describe the energy use of each category. The output was separated into cooling- and heating loads to be used in a geothermal system design program. The geothermal system was considered as an appropriate system to be established in the existing buildings. The results showed that 50% to 100% of the heating loads of the buildings could be covered by geothermal as an alternative to fossil fuels. The introduced reverse method can significantly reduce the time of energy estimation compared to the simulation-based methods. Hence, providing a retrofit proposal to implement renewable energy systems such as geothermal systems would be faster, easier, and affordable for practitioners.

Popular Abstract

Introduction
Greenhouse gas emissions in urban areas represents a key issue for the environment. Replacement of fossil fuels with renewable sources of energy is a potential solution. This research presents development of a fast and practical method for estimating energy use of buildings to facilitate practitioners evaluate the implementation of renewable energy sources.
The Problem
New York City is one of the top 20 megacities in the world. At present, more than 80 % of the required heating is provided through fossil fuels in New York City. However, the city has pledged to reduce its total GHG emissions by 80 % by 2050. Geothermal energy is regarded as one of the most appropriate renewable sources of energy in dense urban areas. Accordingly, this research is focused on establishing geothermal systems in building stock in New York City.
Renewable sources of energy can be established through building energy retrofit. However, retrofit projects should be feasible. Providing an accurate feasibility study needs estimation of building energy use. Conventional methods of estimating building energy use are usually time-consuming and require inputs which are unknown or unavailable to the designers. This research proposes a method to acquire energy estimation for buildings, based on generic and simplified inputs.
The Method
NYC has a publicly accessible dataset for energy and water use of the buildings. It is referred to as benchmarking dataset. In this study, a data-driven energy estimation method based on the information available in the benchmarking dataset has been developed. In contrast to the classic methods, this method calculates the hourly energy use based on the metered total energy use of the building.
This research first defines groups of similar buildings as categories based on building type and vintage. Then an Hourly Energy trend (HET) is defined to describe the hourly pattern of energy use. Finally, the total energy use of the buildings is gathered from the NYC benchmarking dataset.
The estimated hourly energy use is calculated from the HET scaled by the total metered energy use of the buildings. This method provides results divided into the heating load, cooling load, and domestic hot water.
Designing the geothermal system is aimed to maximize the heating load coverage since heating in New York City comes mostly from fossil fuels. The rejected heat from the cooling process is injected into the ground to keep the ground temperature balanced in the long term. As nearly half of the buildings included in the benchmarking dataset have an area between 4,000 m2 to 11,000 m2, this study focuses on the buildings of these sizes.
The Result
Results show that each category of buildings could be identified with a HET. Furthermore, it has been shown that the geothermal system could provide up to 50-100 % of the required thermal loads of the buildings.
The Conclusion
The proposed reverse method provides an hourly energy estimation calibrated with the metered energy use in a quick process. In contrast to the classic methods, it does not need a high level of expertise and several inputs for each building. This method is appropriate for existing buildings since their metered values are available.

• author: Hosseini, Seyedmohammad
• supervisor: Saqib Javed
• organization: Department of Architecture and Built Environment; Division of Energy and Building Design
• course: AEBM01 20211
• year: 2021
• type: H2 - Master's Degree (Two Years)
• subject: Technology and Engineering
• keywords: Energy use prediction, Energy performance simulation, Renewable energy source, data-driven energy estimation, Statistical methods, Reverse method, Regression model, Urban modelling, Energy modelling, Generative modelling, Geothermal heat pump, Borehole Thermal Energy Storage.
• language: English
• id: 9055299
• date added to LUP: 2021-06-16 14:25:07
• date last changed: 2023-07-28 13:09:25